Social Sensing: A New Approach to Understanding Our Socioeconomic Environments

Liu

et al. 2018

Sustainability

Self Cite

Abstract:Together with the emerging popularity of big data in numerous studies, increasing theoretical discussions of the challenges and limitations of such data sources exist. However, there is a clear research gap in the empirical comparison studies on different data sources. The goal of this paper is to use "attractiveness" as a medium to examine the similarity and differences of Social media data (SMD) and survey data in academic research, based on a case study of the Beijing Olympic Forest Park, in Beijing, China. SMD was extracted from two social media platforms and two surveys were conducted to assess the attractiveness of various locations and landscape elements. Data collection, keyword extraction and keyword prioritization were used and compared in the data gathering and analysis process. The findings revealed that SMD and survey data share many similarities. Both data sources confirm that natural ambience is more appreciated than cultural elements, particularly the naturalness of the park. Spaces of practical utility are more appreciated than facilities designed to have cultural meanings and iconic significance. Despite perceived similarities, this study concludes that SMD exhibits exaggerated and aggregated bias. This resulted from the intrinsic character of SMD as volunteered and unstructured data selected through an emotional process rather than from a rational synthesis. Exciting events were reported more often than daily experiences. Reflecting upon the strength and weakness of SMD and survey data, this study would recommend a combined landscape assessment process, which first utilizes SMD to build up an assessment framework, then applies conventional surveys for supplementary and detailed information. This would ultimately result in comprehensive understanding.

Section: Understanding the Similarity Between Smd And Survey Datamentioning

confidence: 99%

Comparing Social Media Data and Survey Data in Assessing the Attractiveness of Beijing Olympic Forest Park

Liu

et al. 2018

Sustainability

Self Cite

“…Some researchers adopted a new term, social sensor data, to indicate individual-level crowdsourced geospatial data, such as check-ins (Foursquare), social media messages (Twitter and Sina-Weibo), and online location-based service reviews (Yelp), which contain rich information about spatial interactions and place semantics in local communities. These social sensor data can provide an opportunity for us to understand our socioeconomic environments in urban areas [23]. Spatio-temporal analysis of social sensor data can provide additional insights into how collective social activity shape urban systems [24].…”

Section: Mapping Urban Dynamics With Social Media and Social Sensor Datamentioning

confidence: 99%

Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Tsou

et al. 2016

Sustainability

Abstract:In fast-growing cities, especially large cities in developing countries, land use types are changing rapidly, and different types of land use are mixed together. It is difficult to assess the land use types in these fast-growing cities in a timely and accurate way. To address this problem, this paper presents a multi-source data mining approach to study dynamic urban land use patterns. Spatiotemporal social media data reveal human activity patterns in different areas, social media text data reflects the topics discussed in different areas, and Points of Interest (POI) reflect the distribution of urban facilities in different regions. Human activity patterns, topics of discussion on social media, and the distribution of urban facilities in different regions were combined and analyzed to infer urban land use patterns. We collected 9.5 million geo-tagged Chinese social media (Sina-Weibo) messages from January 2014 to July 2014 in the urban core areas of Beijing and compared them with 385,792 commercial Points of Interest (POI) from Datatang (a Chinese digital data content provider). To estimate urban land use types and patterns in Beijing, a regular grid of 400 m × 400 m was created to divide the urban core areas into 18,492 cells. By analyzing the temporal frequency trends of social media messages within each cell using K-means clustering algorithm, we identified seven types of land use clusters in Beijing: residential areas, university dormitories, commercial areas, work areas, transportation hubs, and two types of mixed land use areas. Text mining, word clouds, and the distribution analysis of POI were used to verify the estimated land use types successfully. This study can help urban planners create up-to-date land use patterns in an economic way and help us better understand dynamic human activity patterns in a city.

“…The characteristics of a hospital can be better understood by considering its interaction with patients. That is to say, the classification of hospitals in our study relies mainly on human activities, which can reflect the concept of social sensing, revealing the spatial-temporal distribution, link, and process of socio-economic phenomena from human spatial behavior characteristics extracted through spatial big geo-data [12]. Our study provides important insights for policy makers to make adjustments to the current spatial configuration of hospitals in order to provide accessible medical services while helping reduce traffic pressure caused by hospital visits.…”

Section: Discussionmentioning

confidence: 99%

“…In geographical studies, spatial interaction refers to the link between the two places and can usually be quantified using the flows of people, goods, and capital, among others. With the support of big geo-data, we collect a massive volume of data on human movement and flow to quantify spatial interactions between places [12]. Numerous studies have been conducted to examine urban or regional structures using spatial interaction information extracted from social media data [13], phone call records [14], public transportation card records [15], and taxi trajectories [16].…”

Section: Introductionmentioning

confidence: 99%

Investigating Public Facility Characteristics from a Spatial Interaction Perspective: A Case Study of Beijing Hospitals Using Taxi Data

Kong

Liu

et al. 2017

IJGI

Self Cite

Abstract:Services provided by public facilities are essential to people's lives and are closely associated with human mobility. Traditionally, public facility access characteristics, such as accessibility, equity issues and service areas, are investigated mainly based on static data (census data, travel surveys and particular records, such as medical records). Currently, the advent of big data offers an unprecedented opportunity to obtain large-scale human mobility data, which can be used to study the characteristics of public facilities from the spatial interaction perspective. Intuitively, spatial interaction characteristics and service areas of different types and sizes of public facilities are different, but how different remains an open question, so we, in turn, examine this question. Based on spatial interaction, we classify public facilities and explore the differences in facilities. In the research, based on spatial interaction extracted from taxi data, we introduce an unsupervised classification method to classify 78 hospitals in 6 districts of Beijing, and the results better reflect the type of hospital. The findings are of great significance for optimizing the spatial configuration of medical facilities or other types of public facilities, allocating public resources reasonably and relieving traffic pressure.